COVID-19 Trends: Comparing the U.S. with Global Counterparts

Business Task

This project explores how COVID-19 trends in the United States compare to three countries with distinct pandemic response strategies: Italy, Sweden, and South Korea.

The analysis focuses on total confirmed cases, total deaths, and case fatality rates over time.

The goal is to uncover insights into how different national responses may have influenced public health outcomes. This project also serves to demonstrate practical skills in data wrangling, visualization, and interpretation using real-world data.

Introduction

The COVID-19 pandemic had a dramatic and unequal impact around the world. Countries responded with varying degrees of urgency and stringency, from early lockdowns and widespread testing to more relaxed or delayed measures.

This analysis compares the U.S. with Italy, Sweden, and South Korea — three countries that adopted notably different approaches — to identify patterns and evaluate the effectiveness of their respective responses.

covid_clean <- covid %>%
  select(location, date, total_cases, total_deaths) %>%
  filter(location %in% c("United States", "Italy", "Sweden", "South Korea")) %>%
  mutate(date = as.Date(date),
         case_fatality_rate = total_deaths / total_cases)

Data Summary

The dataset comes from Our World in Data’s COVID-19 repository. It contains country-level data on total cases, deaths, and additional features like population, testing, and vaccinations. For this project, we filtered data to focus on the U.S., Italy, Sweden, and South Korea.

To make cross-country comparison more meaningful, we also calculated the case fatality rate: \[ \text{Case Fatality Rate} = \frac{\text{Total Deaths}}{\text{Total Cases}} \]

Note: Differences in testing availability, reporting standards, and time of data collection may influence case and death counts across countries.

Total COVID-19 Cases Over Time

This chart displays how the total number of confirmed COVID-19 cases evolved over time in each country.

p_cases <- ggplot(covid_clean, aes(x = date, y = total_cases, color = location)) +
  geom_line() +
  labs(title = "Total COVID-19 Cases Over Time", x = "Date", y = "Total Cases")

ggplotly(p_cases)

Total COVID-19 Deaths Over Time

This chart illustrates cumulative deaths due to COVID-19, highlighting the most severe outcomes across countries.

p_deaths <- ggplot(covid_clean, aes(x = date, y = total_deaths, color = location)) +
  geom_line() +
  labs(title = "Total COVID-19 Deaths Over Time", x = "Date", y = "Total Deaths")

ggplotly(p_deaths)

Case Fatality Rate Over Time

The case fatality rate (deaths / cases) provides insight into the pandemic’s severity and potential effectiveness of healthcare interventions in each country.

p_cfr <- ggplot(covid_clean, aes(x = date, y = case_fatality_rate, color = location)) +
  geom_line() +
  labs(title = "Case Fatality Rate Over Time",
       x = "Date", y = "Fatality Rate (Deaths / Cases)") +
  scale_y_continuous(labels = scales::percent)

ggplotly(p_cfr)

Average COVID-19 Fatality Rate by Country

heatmap_data <- covid_clean %>%
  group_by(location) %>%
  summarize(avg_cfr = mean(case_fatality_rate, na.rm = TRUE)) %>%
  filter(!is.na(avg_cfr))

# Optional: reorder countries by average fatality rate
heatmap_data$location <- reorder(heatmap_data$location, heatmap_data$avg_cfr)

# Create heatmap
p_heatmap <- ggplot(heatmap_data, aes(x = "", y = location, fill = avg_cfr)) +
  geom_tile(color = "white") +
  scale_fill_gradient(low = "green", high = "red", labels = scales::percent) +
  labs(title = "Average COVID-19 Fatality Rate by Country",
       x = NULL, y = "Country", fill = "Fatality Rate")

# Convert to interactive
ggplotly(p_heatmap)

Key Findings

• The United States experienced the highest total case and death counts throughout the pandemic period.
• South Korea consistently had lower case fatality rates, likely due to early testing, contact tracing, and policy enforcement.
• Italy experienced a steep spike early in the pandemic with a high fatality rate, especially in March–April 2020.
• Sweden’s case fatality rate initially rose higher than South Korea’s or the U.S., potentially influenced by its more relaxed mitigation policies.

These differences suggest that the timing and rigor of public health interventions had a meaningful impact on outcomes.

Limitations

• This analysis does not adjust for population size, healthcare system capacity, or demographic differences.
• Data accuracy may vary between countries due to differences in testing, reporting, and classification of COVID-19 deaths.
• The case fatality rate is a lagging indicator and may underestimate the true mortality risk in rapidly evolving outbreaks.

Conclusion

This comparative analysis of COVID-19 case and fatality data from the United States, South Korea, Sweden, and Italy reveals stark contrasts in pandemic outcomes. South Korea maintained consistently low fatality rates and cumulative deaths per million, aligning with its aggressive early testing and contact tracing strategy. The United States and Italy experienced the highest cumulative deaths and case counts, particularly during the early waves. Sweden’s moderate fatality rate, despite fewer restrictions, shows a different trajectory but still exceeded South Korea’s outcomes.

These findings underscore the measurable impact of national response strategies on fatality outcomes and suggest that proactive containment measures correlate with reduced mortality on a per capita basis.

Note: This analysis focuses solely on reported COVID-19 cases and deaths. It does not account for indirect impacts of the pandemic, such as excess deaths from disrupted healthcare access or economic hardship. These factors are important but outside the scope of this data-driven analysis.

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Source: Our World in Data – COVID-19 dataset